apify-content-analytics
Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.
72
66%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/apify-content-analytics/SKILL.mdQuality
Discovery
67%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description is strong in specificity and distinctiveness, clearly naming concrete actions and specific platforms that carve out a well-defined niche. However, it lacks an explicit 'Use when...' clause, which limits its completeness score, and could benefit from additional natural trigger terms that users commonly use when requesting social media analytics.
Suggestions
Add a 'Use when...' clause such as 'Use when the user asks about social media analytics, post performance, follower growth, or marketing campaign results.'
Include broader natural trigger terms like 'social media', 'analytics', 'likes', 'followers', 'views', 'impressions', and 'social media marketing' to improve keyword coverage.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Track engagement metrics', 'measure campaign ROI', and 'analyze content performance', along with specific platforms (Instagram, Facebook, YouTube, TikTok). | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific actions and platforms, but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this dimension at 2 per the rubric. | 2 / 3 |
Trigger Term Quality | Includes good platform names and terms like 'engagement metrics', 'campaign ROI', and 'content performance', but misses common user variations like 'social media analytics', 'likes', 'followers', 'views', 'social media', or 'posts'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of social media platforms (Instagram, Facebook, YouTube, TikTok) with engagement/ROI/performance analysis creates a clear niche that is unlikely to conflict with general analytics or non-social-media skills. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a functional skill with strong actionability—concrete commands, clear Actor selection table, and specific error handling. Its main weaknesses are the lack of validation/feedback loops in the workflow (e.g., verifying Actor run success before summarizing) and some verbosity from the large inline table and generic boilerplate limitations section. Tightening the content and adding a validation checkpoint would meaningfully improve it.
Suggestions
Add an explicit validation step after Step 4 to check if the Actor run succeeded before proceeding to summarize (e.g., check exit code or output file existence), and add a retry loop for failed runs.
Move the large Actor lookup table to a separate reference file (e.g., ACTORS.md) and keep only a brief summary or top 3-4 most common use cases inline.
Remove the generic 'Limitations' section boilerplate—these are general Claude behaviors that don't need to be stated and waste tokens.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The large Actor table is useful reference but could be more compact. The limitations section contains generic boilerplate that doesn't add value. Some sections like 'Ask User Preferences' are somewhat padded. Overall mostly efficient but has room for tightening. | 2 / 3 |
Actionability | Provides fully executable bash commands for fetching schemas, running actors in multiple output formats, and clear error handling with specific solutions. The commands are copy-paste ready with clear placeholder substitution patterns. | 3 / 3 |
Workflow Clarity | The 5-step workflow is clearly sequenced with a progress checklist, but lacks validation checkpoints. There's no step to verify the Actor ran successfully before summarizing, no feedback loop for retrying failed runs, and no validation of the input JSON before execution. | 2 / 3 |
Progressive Disclosure | The skill references external scripts (run_actor.js) and tools appropriately, but the massive Actor lookup table could be split into a separate reference file. The content is reasonably organized with clear sections but the inline table makes the skill longer than necessary. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- sickn33/antigravity-awesome-skills
- Commit
1a9f5ac
- Reviewed
Table of Contents
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